Macrophage polarization involves a coordinated metabolic and transcriptional rewiring that is only partially understood. By using an integrated high-throughput transcriptional-metabolic profiling and ...analysis pipeline, we characterized systemic changes during murine macrophage M1 and M2 polarization. M2 polarization was found to activate glutamine catabolism and UDP-GlcNAc-associated modules. Correspondingly, glutamine deprivation or inhibition of N-glycosylation decreased M2 polarization and production of chemokine CCL22. In M1 macrophages, we identified a metabolic break at Idh, the enzyme that converts isocitrate to alpha-ketoglutarate, providing mechanistic explanation for TCA cycle fragmentation. 13C-tracer studies suggested the presence of an active variant of the aspartate-arginosuccinate shunt that compensated for this break. Consistently, inhibition of aspartate-aminotransferase, a key enzyme of the shunt, inhibited nitric oxide and interleukin-6 production in M1 macrophages, while promoting mitochondrial respiration. This systems approach provides a highly integrated picture of the physiological modules supporting macrophage polarization, identifying potential pharmacologic control points for both macrophage phenotypes.
•Glutamine deprivation affects M2 polarization but not M1 polarization•UDP-GlcNAc biosynthesis and N-glycosylation are important for M2 polarization•There is no reverse or direct flow through Idh or malic enzyme in M1 macrophages•Aspartate-arginosuccinate shunt connects the NO and TCA cycles in M1 polarization
Polarization of macrophages involves a metabolic and transcriptional rewiring that is only partially understood. Artyomov and colleagues used an integrated high-throughput transcriptional-metabolic profiling and analysis pipeline to identify metabolic modules that support macrophage polarization and function.
Accelerated glucose metabolism is a common feature of cancer cells. Hexokinases catalyze the first committed step of glucose metabolism. Hexokinase 2 (HK2) is expressed at high level in cancer cells, ...but only in a limited number of normal adult tissues. Using Hk2 conditional knockout mice, we showed that HK2 is required for tumor initiation and maintenance in mouse models of KRas-driven lung cancer, and ErbB2-driven breast cancer, despite continued HK1 expression. Similarly, HK2 ablation inhibits the neoplastic phenotype of human lung and breast cancer cells in vitro and in vivo. Systemic Hk2 deletion is therapeutic in mice bearing lung tumors without adverse physiological consequences. Hk2 deletion in lung cancer cells suppressed glucose-derived ribonucleotides and impaired glutamine-derived carbon utilization in anaplerosis.
Display omitted
•HK2 deletion exerts resistance to oncogenic transformation•HK2 deletion selectively targets cancer cells•Systemic deletion of HK2 is well tolerated and therapeutic for cancer l•HK2 is required for glucose-derived and glutamine in utilization in anaplerosis
An unfolded state ensemble is generated by using a self-avoiding statistical coil model that is based on backbone conformational frequencies in a coil library, a subset of the Protein Data Bank. The ...model reproduces two apparently contradicting behaviors observed in the chemically denatured state for a variety of proteins, random coil scaling of the radius of gyration and the presence of significant amounts of local backbone structure (NMR residual dipolar couplings). The most stretched members of our unfolded ensemble dominate the residual dipolar coupling signal, whereas the uniformity of the sign of the couplings follows from the preponderance of polyproline II and β conformers in the coil library. Agreement with the NMR data substantially improves when the backbone conformational preferences include correlations arising from the chemical and conformational identity of neighboring residues. Although the unfolded ensembles match the experimental observables, they do not display evidence of native-like topology. By providing an accurate representation of the unfolded state, our statistical coil model can be used to improve thermodynamic and kinetic modeling of protein folding.
T lymphocytes (T cells) orchestrate adaptive immune responses that clear pathogens from infected hosts. T cells recognize short peptides (p) derived from antigenic proteins bound to protein products ...of the MHC genes. Recognition occurs when T cell receptor (TCR) proteins expressed on T cells bind sufficiently strongly to antigen-derived pMHC complexes on the surface of antigen-presenting cells. A diverse repertoire of self-pMHC-tolerant TCR sequences is shaped during development of T cells in the thymus by processes called positive and negative selection. Combining computational models and analysis of experimental data, we parsed the contributions of positive and negative selection to the design of TCR sequences that recognize antigenic peptides with specificity, yet also exhibit cross-reactivity. A dominant role for negative selection in mediating antigen specificity of mature T cells and a molecular mechanism for TCR recognition of antigen are described.
Crystals of many important biological macromolecules diffract to limited resolution, rendering accurate model building and refinement difficult and time-consuming. We present a torsional optimization ...protocol that is applicable to many such situations and combines Protein Data Bank-based torsional optimization with real-space refinement against the electron density derived from crystallography or cryo-electron microscopy. Our method converts moderate- to low-resolution structures at initial (e.g., backbone trace only) or late stages of refinement to structures with increased numbers of hydrogen bonds, improved crystallographic R-factors, and superior backbone geometry. This automated method is applicable to DNA-binding and membrane proteins of any size and will aid studies of structural biology by improving model quality and saving considerable effort. The method can be extended to improve NMR and other structures. Our backbone score and its sequence profile provide an additional standard tool for evaluating structural quality.
The physical content of and, in particular, the nonlinear contributions from the Langevin-Debye model are illustrated using two applications. First, we provide an improvement in the Langevin-Debye ...model currently used in some implicit solvent models for computer simulations of solvation free energies of small organic molecules, as well as of biomolecular folding and binding. The analysis is based on the implementation of a charge-dependent Langevin-Debye (qLD) model that is modified by subsequent corrections due to Onsager and Kirkwood. Second, the physical content of the model is elucidated by discussing the general treatment within the LD model of the self-energy of a charge submerged in a dielectric medium for three different limiting conditions and by considering the nonlinear response of the medium. The modified qLD model is used to refine an implicit solvent model (previously applied to protein dynamics). The predictions of the modified implicit solvent model are compared with those from explicit solvent molecular dynamics simulations for the equilibrium conformational populations of 1,2-dimethoxyethane (DME), which is the shortest ether molecule to reproduce the local conformational properties of polyethylene oxide, a polymer with tremendous technological importance and a wide variety of applications. Because the conformational population preferences of DME change dramatically upon solvation, DME is a good test case to validate our modified qLD model. The present analysis of the modified qLD model provides the motivation and tools for studying a wide variety of other interesting systems with heterogeneous dielectric properties and spatial anisotropy.
Since the demonstration that the sequence of a protein encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines, ...including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse-grained model without information concerning homology or explicit side chains can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (φ,ψ) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.
Cultured cells convert glucose to lactate, and glutamine is the major source of tricarboxylic acid (TCA)-cycle carbon, but whether the same metabolic phenotype is found in tumors is less studied. We ...infused mice with lung cancers with isotope-labeled glucose or glutamine and compared the fate of these nutrients in tumor and normal tissue. As expected, lung tumors exhibit increased lactate production from glucose. However, glutamine utilization by both lung tumors and normal lung was minimal, with lung tumors showing increased glucose contribution to the TCA cycle relative to normal lung tissue. Deletion of enzymes involved in glucose oxidation demonstrates that glucose carbon contribution to the TCA cycle is required for tumor formation. These data suggest that understanding nutrient utilization by tumors can predict metabolic dependencies of cancers in vivo. Furthermore, these data argue that the in vivo environment is an important determinant of the metabolic phenotype of cancer cells.
Display omitted
•Metabolic phenotyping of tumors can identify essential metabolic pathways•Kras-driven lung tumors require pyruvate carboxylase and pyruvate dehydrogenase•Kras-driven lung tumors are less dependent on glutaminase than cultured cells•Tissue environment is an important determinant of tumor metabolic phenotypes
Davidson et al. find that lung cancer cells in culture use nutrients differently than lung tumors, especially regarding glutamine metabolism. This difference in metabolic phenotype reflects how the tumor environment determines nutrient dependency and highlights the importance of studying cancer metabolism in a physiological context.
Cancer cells adapt their metabolic processes to drive macromolecular biosynthesis for rapid cell growth and proliferation. RNA interference (RNAi)-based loss-of-function screening has proven powerful ...for the identification of new and interesting cancer targets, and recent studies have used this technology in vivo to identify novel tumour suppressor genes. Here we developed a method for identifying novel cancer targets via negative-selection RNAi screening using a human breast cancer xenograft model at an orthotopic site in the mouse. Using this method, we screened a set of metabolic genes associated with aggressive breast cancer and stemness to identify those required for in vivo tumorigenesis. Among the genes identified, phosphoglycerate dehydrogenase (PHGDH) is in a genomic region of recurrent copy number gain in breast cancer and PHGDH protein levels are elevated in 70% of oestrogen receptor (ER)-negative breast cancers. PHGDH catalyses the first step in the serine biosynthesis pathway, and breast cancer cells with high PHGDH expression have increased serine synthesis flux. Suppression of PHGDH in cell lines with elevated PHGDH expression, but not in those without, causes a strong decrease in cell proliferation and a reduction in serine synthesis. We find that PHGDH suppression does not affect intracellular serine levels, but causes a drop in the levels of α-ketoglutarate, another output of the pathway and a tricarboxylic acid (TCA) cycle intermediate. In cells with high PHGDH expression, the serine synthesis pathway contributes approximately 50% of the total anaplerotic flux of glutamine into the TCA cycle. These results reveal that certain breast cancers are dependent upon increased serine pathway flux caused by PHGDH overexpression and demonstrate the utility of in vivo negative-selection RNAi screens for finding potential anticancer targets.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with ...transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM ('genes and metabolites'): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/.
PDF
